Name
Title | ||
|---|---|---|
Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks |
Abstract | ||
|---|---|---|
•A co-representation learning framework to maximally utilize information from data.•Complementary information extraction via cross-entropy and deep metric learning.•Informative triplet sampling and soft-multi-pair loss to accelerate deep metric learning.•State-of-the-art performance on five benchmark datasets for much reduced data usage.•New state-of-the-art classification performance on five digital pathology datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.media.2020.101859 | Medical Image Analysis |
Keywords | DocType | Volume |
Digital pathology,Co-representation learning,Deep metric learning,Informative triplet sampling,Soft-multi-pair loss,Limited annotations,Nuclei classification,Mitosis detection,Tissue type classification | Journal | 67 |
ISSN | Citations | PageRank |
1361-8415 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pushpak Pati | 1 | 1 | 1.74 |
| Antonio Foncubierta-Rodriguez | 2 | 156 | 17.13 |
| Orçun Göksel | 3 | 31 | 8.92 |
| Maria Gabrani | 4 | 0 | 0.34 |